{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from potes1DCNN import reshape_folds\n",
    "import tables\n",
    "import ast # for list import\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "foldname='fold0_noFIR'\n",
    "#load_path='/media/taufiq/Data/heart_sound/models/fold3 2017-12-20 14:43:16.922783/weights.0080-0.8165.hdf5'\n",
    "#load_path='/media/taufiq/Data/heart_sound/models/fold2 2017-12-20 12:14:09.278081/weights.0077-0.8860.hdf5'\n",
    "#load_path='/media/taufiq/Data/heart_sound/remoteIICTweights/fold1 2017-12-20 15:26:47.011204/weights.0091-0.8898.hdf5'\n",
    "#load_path='/media/taufiq/Data/heart_sound/models/fold0 2017-12-20 16:10:31.624027/weights.0189-0.7399.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold1_cont 2018-01-10 18:51:56.259053/weights.0142-0.9012.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold0 2018-01-18 14:31:39.174997/weights.0135-0.7425.hdf5'\n",
    "# load_path ='/media/taufiq/Data/heart_sound/models/fold1 2018-01-17 17:08:20.854227/weights.0195-0.8244.hdf5'\n",
    "# load_path ='/media/taufiq/Data/heart_sound/models/fold2 2018-01-20 09:14:01.023245/weights.0164-0.8241.hdf5'\n",
    "# load_path ='/media/taufiq/Data/heart_sound/models/fold3 2018-01-19 01:17:49.485829/weights.0192-0.8096.hdf5'\n",
    "load_path='/media/taufiq/Data/heart_sound/models/fold0_noFIR 2018-01-16 17:25:30.579232/weights.0195-0.7422.hdf5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed=1\n",
    "batch_size=64\n",
    "fold_dir='/media/taufiq/Data/heart_sound/feature/potes_1DCNN/balancedCV/folds/'\n",
    "log_dir= '/media/taufiq/Data/heart_sound/Heart_Sound/codes/logs/'\n",
    "bn_momentum = 0.99\n",
    "eps= 1.1e-5\n",
    "bias=False\n",
    "l2_reg=0.\n",
    "l2_reg_dense=0.\n",
    "kernel_size=5\n",
    "maxnorm=10000.\n",
    "dropout_rate=0.5\n",
    "dropout_rate_dense=0.\n",
    "padding='valid'\n",
    "activation_function='relu'\n",
    "subsam=2\n",
    "\n",
    "lr=0.0007\n",
    "lr_decay=1e-8\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from potes1DCNN import heartnet\n",
    "model = heartnet(activation_function,bn_momentum,bias,dropout_rate,dropout_rate_dense,\n",
    "\t\teps,kernel_size,l2_reg,l2_reg_dense,load_path,lr,lr_decay,maxnorm,\n",
    "\t\tpadding,random_seed,subsam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data No tConv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 4, 1)\n",
      "(7324, 1)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-38cb3b80df5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m                 \u001b[0my_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_qual\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreshape_folds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_qual\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'loading complete'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/taufiq/Data/heart_sound/Heart_Sound/codes/potes1DCNN.pyc\u001b[0m in \u001b[0;36mreshape_folds\u001b[0;34m(x_train, x_val, y_train, y_val)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m         \u001b[0mv1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0mv2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0mv3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "feat = tables.open_file(fold_dir+foldname+'.mat')\n",
    "x_val = feat.root.valX[:]\n",
    "y_val = feat.root.valY[0,:]\n",
    "val_parts = feat.root.val_parts[0,:]\n",
    "y_qual = feat.root.valY[1,:]\n",
    "\n",
    "for i in range(y_val.shape[0]):\n",
    "\tif y_val[i]==-1:\n",
    "\t\ty_val[i]=0\n",
    "\t\t\n",
    "_,y_qual,x_val,y_val=reshape_folds(np.random.randn(4,4,4),x_val,y_qual,y_val)\n",
    "print 'loading complete'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data with tConv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80531, 2500, 1)\n",
      "(80531, 1)\n",
      "(7324, 2500, 1)\n",
      "(7324, 1)\n",
      "loading complete\n"
     ]
    }
   ],
   "source": [
    "import tables\n",
    "from heartnet_v1 import reshape_folds\n",
    "feat = tables.open_file(fold_dir + foldname + '.mat')\n",
    "x_train = feat.root.trainX[:]\n",
    "y_train = feat.root.trainY[0, :]\n",
    "x_val = feat.root.valX[:]\n",
    "y_val = feat.root.valY[0, :]\n",
    "train_parts = feat.root.train_parts[:]\n",
    "val_parts = feat.root.val_parts[0, :]\n",
    "\n",
    "for i in range(0, y_train.shape[0]):\n",
    "    if y_train[i] == -1:\n",
    "        y_train[i] = 0  ## Label 0 for normal 1 for abnormal\n",
    "for i in range(0, y_val.shape[0]):\n",
    "    if y_val[i] == -1:\n",
    "        y_val[i] = 0\n",
    "x_train, y_train, x_val, y_val = reshape_folds(x_train, x_val, y_train, y_val)\n",
    "print \"loading complete\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "values initialized\n"
     ]
    }
   ],
   "source": [
    "from heartnet_v1 import heartnet, reshape_folds\n",
    "\n",
    "random_seed=1\n",
    "batch_size=64\n",
    "fold_dir='/media/taufiq/Data/heart_sound/feature/potes_1DCNN/balancedCV/folds/'\n",
    "log_dir= '/media/taufiq/Data/heart_sound/Heart_Sound/codes/logs/'\n",
    "bn_momentum = 0.99\n",
    "eps= 1.1e-5\n",
    "bias=False\n",
    "l2_reg=0.\n",
    "l2_reg_dense=0.\n",
    "kernel_size=5\n",
    "maxnorm=10000.\n",
    "dropout_rate=0.5\n",
    "dropout_rate_dense=0.\n",
    "padding='valid'\n",
    "activation_function='relu'\n",
    "subsam=2\n",
    "load_path=False\n",
    "lr=0.0012843784 \n",
    "lr_decay=0.0001132885\n",
    "FIR_train=True\n",
    "num_filt=(8,4)\n",
    "num_dense=20\n",
    "\n",
    "model = heartnet(activation_function, bn_momentum, bias, dropout_rate, dropout_rate_dense,\n",
    "             eps, kernel_size, l2_reg, l2_reg_dense, load_path, lr, lr_decay, maxnorm,\n",
    "             padding, random_seed, subsam, num_filt, num_dense, FIR_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.49964428]\n",
      " [ 0.50020778]\n",
      " [ 0.5005998 ]\n",
      " ..., \n",
      " [ 0.49970943]\n",
      " [ 0.49816167]\n",
      " [ 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(x_val)\n",
    "print y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(301,)\n"
     ]
    }
   ],
   "source": [
    "true = []\n",
    "pred=[]\n",
    "qual=[]\n",
    "start_idx = 0\n",
    "print val_parts.shape\n",
    "for s in val_parts:\t\n",
    "\t\n",
    "\tif s==0:\t\t## for e1,32,39,44 in validation0 there was no cardiac cycle\n",
    "            pred.append(0)\n",
    "            true.append(0)\n",
    "            qual.append(0)\n",
    "            continue \n",
    "\t#~ print \"part {} start {} stop {}\".format(s,start_idx,start_idx+int(s)-1)\n",
    "\t\t\t\t\t\n",
    "\ttemp_ = np.mean(y_val[start_idx:start_idx+int(s)-1])\n",
    "\ttemp = np.mean(y_pred[start_idx:start_idx+int(s)-1,:])\n",
    "# \tqual_ = np.mean(y_qual[start_idx:start_idx+int(s)-1,:])\n",
    "\t\n",
    "\tpred.append(temp)\n",
    "\ttrue.append(temp_)\n",
    "# \tqual.append(qual_)\n",
    "\t\n",
    "\tstart_idx = start_idx + int(s)\t\t\n",
    "\n",
    "true=np.array(true)\n",
    "pred=np.array(pred)\n",
    "# qual=np.array(qual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import List of recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_path='/media/taufiq/Data/heart_sound/feature/potes_1DCNN/balancedCV/'+'validation'+foldname[4]+'.txt'\n",
    "import pandas as pd\n",
    "val_list=pd.read_csv(list_path,header=None)\n",
    "val_list=val_list.values\n",
    "flat_list = [item[0] for sublist in val_list for item in sublist]\n",
    "flat_list=sorted(flat_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>Index</th>\n",
       "      <th>True</th>\n",
       "      <th>Pred</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499666</td>\n",
       "      <td>0.500334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499731</td>\n",
       "      <td>0.500269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499234</td>\n",
       "      <td>0.500766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.499097</td>\n",
       "      <td>0.500903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.498035</td>\n",
       "      <td>0.501965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  Index  True      Pred     Error\n",
       "0  a      0   1.0  0.499666  0.500334\n",
       "1  a      1   1.0  0.499731  0.500269\n",
       "2  a      2   1.0  0.499234  0.500766\n",
       "3  a      3   1.0  0.499097  0.500903\n",
       "4  a      4   1.0  0.498035  0.501965"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_list=pd.DataFrame(flat_list)\n",
    "flat_list['Index']=pd.DataFrame(range(0,val_parts.shape[0]))\n",
    "flat_list['True']=pd.DataFrame(true)\n",
    "flat_list['Pred']=pd.DataFrame(pred)\n",
    "flat_list['Error']=pd.DataFrame(abs(true-pred))\n",
    "flat_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "0.510204081633\n",
      "0.428571428571\n",
      "0.7\n",
      "0.358490566038\n",
      "nan\n",
      "0.458471760797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in divide\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for dataset in ['a','b','c','d','e','f']: \n",
    "    print sum(flat_list['Error'][flat_list[0]==dataset].values<.5)/np.float32(flat_list[flat_list[0]==dataset].shape[0])\n",
    "print sum(flat_list['Error']<.5)/301."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot different datasets with color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a-Normal-26\n",
      "b-Normal-31\n",
      "c-Normal-3\n",
      "d-Normal-1\n",
      "e-Normal-53\n",
      "f-Normal-nan\n",
      "a-Abnormal-6\n",
      "b-Abnormal-17\n",
      "c-Abnormal-1\n",
      "d-Abnormal-3\n",
      "e-Abnormal-5\n",
      "f-Abnormal-nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f240a6f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize']=(30,10)\n",
    "for label,marker,name in zip([0,1],['o','^'],['Normal','Abnormal']):\n",
    "    for dataset,color in zip(['a','b','c','d','e','f'],['b','r','g','y','c','m']):\n",
    "        x = flat_list['Index'][flat_list[0]==dataset][flat_list['True']==label]\n",
    "        y = flat_list['Pred'][flat_list[0]==dataset][flat_list['True']==label]\n",
    "        print '{}-{}-{}'.format(dataset,name,np.sum(y<.5))\n",
    "        plt.scatter(x,y,40,color=color,label=dataset+' '+name,marker=marker)\n",
    "plt.xticks(range(0,len(flat_list),20),rotation='vertical')\n",
    "plt.xlim(0,len(flat_list))\n",
    "plt.ylim(0,1)\n",
    "#plt.title('Absolute error vs recordings for '+foldname)\n",
    "#plt.xlabel('Recording #')\n",
    "#plt.ylabel('Absolute Error |True-Pred|')\n",
    "# plt.yscale('log',nonposy='clip')\n",
    "#plt.xscale('log')\n",
    "#plt.ylim(1,100)\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fb4155d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize']=(70,30)\n",
    "plt.stem(range(0,true.shape[0]),((true-pred)**2)**.5)\n",
    "plt.stem(range(0,true.shape[0]),true,'y--',linewidth=.002)\n",
    "plt.xticks(range(0,len(flat_list)),flat_list,rotation='vertical')\n",
    "plt.title('Absolute error vs recordings for '+foldname)\n",
    "plt.xlim(0,len(flat_list))\n",
    "plt.ylim(0,1)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 Abnormal recordings strongly mislabeled\n",
      "5 Normal recordings strongly mislabeled\n"
     ]
    }
   ],
   "source": [
    "tolerance_thresh=.6\n",
    "print \"{} Abnormal recordings strongly mislabeled\".format(sum((((true-pred)**2)**.5)*true>tolerance_thresh))\n",
    "print \"{} Normal recordings strongly mislabeled\".format(sum((((true-pred)**2)**.5)*(1-true)>tolerance_thresh))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
